Large-scale value-based non-linear models often require large numbers of decision variables and constraints (e.g., over a million). Multi-objective Evolutionary Algorithms (MOEA) may fail when they have such a large number of decision variables and constraints. Often times in these MOEA, the computational bandwidth needed to search a relatively large decision variable space and to evaluate whether a potential solution violates a constraint may be relatively high, where the cause of the constraint may be too complex to facilitate driving to feasibility. In some cases, determining whether potential solutions violate constraints may require similar or even more computational bandwidth than evaluations of potential solutions on the basis of one or more objectives.